1 research outputs found
Conservative collision prediction and avoidance for stochastic trajectories in continuous time and space
Existing work in multi-agent collision prediction and avoidance typically
assumes discrete-time trajectories with Gaussian uncertainty or that are
completely deterministic. We propose an approach that allows detection of
collisions even between continuous, stochastic trajectories with the only
restriction that means and variances can be computed. To this end, we employ
probabilistic bounds to derive criterion functions whose negative sign provably
is indicative of probable collisions. For criterion functions that are
Lipschitz, an algorithm is provided to rapidly find negative values or prove
their absence. We propose an iterative policy-search approach that avoids prior
discretisations and yields collision-free trajectories with adjustably high
certainty. We test our method with both fixed-priority and auction-based
protocols for coordinating the iterative planning process. Results are provided
in collision-avoidance simulations of feedback controlled plants.Comment: This preprint is an extended version of a conference paper that is to
appear in \textit{Proceedings of the 13th International Conference on
Autonomous Agents and Multiagent Systems (AAMAS 2014)